Moiré patterns, often observed as rippling effects when photographing a screen with a phone, are familiar to many people. In physics, however, these patterns are not merely visual artifacts but powerful tools for material control and tuning. By stacking graphene layers with a specific twist angle, Moiré superlattices can be created, enabling novel energy band modulations. This project extends the concept of Moiré superlattices to non-Hermitian photonic devices, offering unprecedented control over multi-degree coupling of light transmission. This advancement enables dynamic light manipulation capabilities far beyond conventional devices. Immediate applications include hyperband holography and ultrasensitive sensing. By bridging cutting-edge physics concepts with state-of-the-art nanomaterials, this project aims to pave the way for next-generation integrated photonic chips, optical imaging systems, and advanced biosensing technologies.

We will develop near-optimal cellular radio frequency (RF) architectures operating in the mid-band, aiming to enhance massive multiple-input multiple-output (MIMO) technology. We will derive the spatial degrees-of-freedom of the RF channel, which will give guidance on the number of needed baseband units and the optimal radiation patterns of the antenna array in the base station. In addition, we will develop signal-guiding metasurfaces to enhance the multipath richness of the propagation environment. The optimality of the volumetric antenna arrays and signal-guiding metasurfaces will be numerically evaluated and finally verified experimentally through fabrication of the RF designs and field measurements. The study will focus on the mid-band spectrum spanning between 6 and 20 GHz which is relevant and useful to the present and future needs of the cellular industry.

The REMUS project aims to enhance historical music recordings using advanced generative models. Imagine hearing century-old musical treasures with the clarity and richness of todays high-quality audio - thats our goal. Utilizing state-of-the-art generative models, grounded in deep neural networks, our initiative will not only remove hiss and crackles from old records but will also restore lost high-frequency sounds and repair damage such as scratches and fade-outs on gramophone and vinyl records. This four-year journey will blend technology with cultural heritage, democratizing audio restoration, offering new tools to musicologists, and preserving our musical past for future generations. The REMUS project is more than just a technological feat; it is about forging a bridge between the past and the present, ensuring historical music will resonate with todays audiences.

Precision nanofabrication techniques are essential for assembling nanomaterials with functionalities that promise to revolutionize electronics, medical advancements and optics. The scanning probe microscopy (SPM) techniques have emerged as a promising approach to fabricate nanostructures. However, the selection of proper parameters is time-consuming and requires extensive domain knowledge. Therefore, new efficient and autonomous SPM techniques are needed to reduce the reliance on user supervision and learn optimal strategies for new systems. In this proposal, I will develop deep learning approaches to automate synthesizing a whole Ullmann reaction with the precursor Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (ZnBr2Me4DPP)) on TMDs which includes the dissociation of C-Br bonds and association of C-C bonds and movement of fragments, and the assembly of large nanostructures in STM by combining DFT calculations and machine learning interatomic potentials.

Extreme events as a result of climate change and rapid urbanization pose major challenges for the transportation infrastructure that connect people and activity across space. In this project, I will study how road transportation networks and travel behavior cope with such disruptions to mobility, and what makes cities either more or less resilient to them in the long term. In particular, the project will use publicly available real-time data on travel times, weather and road incidents for countries around the world to study how unexpected disruptions slow down travel, how road users choose to adapt (e.g. by switching to alternative routes, departure times or travel modes), and how their choices affect the rest of the travel network (e.g. via congestion spillovers). I will then quantify the welfare gains from having a more resilient travel network, while identifying features of routes and the broader travel network that help mitigate the effects of observed disruptions.